In many tasks, classifiers play a fundamental role in the way an
agent behaves. Most rational agents collect sensor data from the environment,
classify it, and act based on that classification. Recently,
deep neural networks (DNNs) have become the dominant approach
to develop classifiers due to their excellent performance. When
training and evaluating the performance of DNNs, it is normally
assumed that the cost of all misclassification errors are equal. However,
this is unlikely to be true in practice. Incorrect classification
predictions can cause an agent to take inappropriate actions. The
costs of these actions can be asymmetric, vary from agent-to-agent,
and depend on context. In this paper, we discuss the importance of
considering risk and uncertainty quantification together to reduce
agents’ cost of making misclassifications using deep classifiers.

Not all Mistakes are Equal

Learning and Adaptation

technical paper

AAMAS is the leading scientific conference for research in autonomous agents and multi-agent systems. The AAMAS conference series was initiated in 2002 as the merging of three respected scientific meetings: the International Conference on Multi-Agent Systems (ICMAS), the International Workshop on Agent Theories, Architectures, and Languages (ATAL), and the International Conference on Autonomous Agents (AA). The aim of the joint conference is to provide a single, high-profile, internationally-respected archival forum for scientific research in the theory and practice of autonomous agents and multi-agent systems.

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AAMAS 2020

AAMAS is the leading scientific conference for research in autonomous agents and multi-agent systems. The AAMAS conference series was initiated in 2002 as the merging of three respected scientific meetings: the International Conference on Multi-Agent Systems (ICMAS), the International Workshop on Agent Theories, Architectures, and Languages (ATAL), and the International Conference on Autonomous Agents (AA). 

Simon Julier

1

Presentations

Not all Mistakes are Equal